Mesoscale and Nanoscale Physics (cond-mat.mes-hall)

Abstract: The out-of-thermal-equilibrium Casimir-Polder force between nanoparticles and dielectric substrates coated with gapped graphene is considered in the framework of the Dirac model using the formalism of the polarization tensor. This is an example of physical phenomena violating the time-reversal symmetry. After presenting the main points of the used formalism, we calculate two contributions to the Casimir-Polder force acting on a nanoparticle on the source side of a fused silica glass substrate coated with gapped graphene, which is either cooler or hotter than the environment. The total nonequilibrium force magnitudes are computed as a function of separation for different values of the energy gap and compared with those from an uncoated plate and with the equilibrium force in the presence of graphene coating. According to our results, the presence of a substrate increases the magnitude of the nonequlibrium force. The force magnitude becomes larger with higher and smaller with lower temperature of the graphene-coated substrate as compared to the equilibrium force at the environmental temperature. It is shown that with increasing energy gap the magnitude of the nonequilibrium force becomes smaller, and the graphene coating makes a lesser impact on the force acting on a nanoparticle from the uncoated substrate. Possible applications of the obtained results are discussed.

Abstract: Magnetic skyrmions are nano-sized topologically non-trivial spin textures that can be moved by external stimuli such as spin currents and internal stimuli such as spatial gradients of a material parameter. Since the total energy of a skyrmion depends linearly on most of these parameters, like the perpendicular magnetic anisotropy, the exchange constant, or the Dzyaloshinskii-Moriya interaction strength, a skyrmion will move uniformly in a weak parameter gradient. In this paper, we show that the linear behavior changes once the gradients are strong enough so that the magnetic profile of a skyrmion is significantly altered throughout the propagation. In that case, the skyrmion experiences acceleration and moves along a curved trajectory. Furthermore, we show that when spin-orbit torques and material parameter gradients trigger a skyrmion motion, it can move on a straight path along the current or gradient direction. We discuss the significance of suppressing the skyrmion Hall effect for spintronic and neuromorphic applications of skyrmions. Lastly, we extend our discussion and compare it to a gradient generated by the Dzyaloshinskii-Moriya interaction.

Abstract: Measuring large electrical resistances forms an essential part of common applications such as insulation testing, but suffers from a fundamental problem: the larger the resistance, the less sensitive a canonical ohmmeter is. Here we develop a conceptually different electronic sensor by exploiting the topological properties of non-Hermitian matrices, whose eigenvalues can show an exponential sensitivity to perturbations. The ohmmeter is realized in an multi-terminal, linear electric circuit with a non-Hermitian conductance matrix, where the target resistance plays the role of the perturbation. We inject multiple currents and measure a single voltage in order to directly obtain the value of the resistance. The relative accuracy of the device increases exponentially with the number of terminals, and for large resistances outperforms a standard measurement by over an order of magnitude. Our work paves the way towards leveraging non-Hermitian conductance matrices in high-precision sensing.

Abstract: Extracting Hamiltonian parameters from available experimental data is a challenge in quantum materials. In particular, real-space spectroscopy methods such as scanning tunneling spectroscopy allow probing electronic states with atomic resolution, yet even in those instances extracting effective Hamiltonian is an open challenge. Here we show that impurity states in modulated systems provide a promising approach to extracting non-trivial Hamiltonian parameters of a quantum material. We show that by combining the real-space spectroscopy of different impurity locations in a moire topological superconductor, modulations of exchange and superconducting parameters can be inferred via machine learning. We demonstrate our strategy with a physically-inspired harmonic expansion combined with a fully-connected neural network that we benchmark against a conventional convolutional architecture. We show that while both approaches allow extracting exchange modulations, only the former approach allows inferring the features of the superconducting order. Our results demonstrate the potential of machine learning methods to extract Hamiltonian parameters by real-space impurity spectroscopy as local probes of a topological state.

Abstract: Surface-enhanced Raman spectroscopy (SERS) is enabled by local surface plasmon resonances (LSPRs) in metallic nanogaps. When SERS is excited by direct illumination of the nanogap, the background heating of lattice and electrons can prevent further manipulation of the molecules. To overcome this issue, we report SERS in electromigrated gold molecular junctions excited remotely: surface plasmon polaritons (SPPs) are excited at nearby gratings, propagate to the junction, and couple to the local nanogap plasmon modes. Like direct excitation, remote excitation of the nanogap can generate both SERS emission and an open-circuit photovoltage (OCPV). We compare SERS intensity and OCPV in both direct and remote illumination configurations. SERS spectra obtained by remote excitation are much more stable than those obtained through direct excitation when photon count rates are comparable. By statistical analysis of 33 devices, coupling efficiency of remote excitation is calculated to be around 10%, consistent with the simulated energy flow.